complete inpainting ability using inpaint_image and inpaint_mask passed into sample function for decoder

README.md

@@ -1049,8 +1049,8 @@ Once built, images will be saved to the same directory the command is invoked
 - [x] test out grid attention in cascading ddpm locally, decide whether to keep or remove https://arxiv.org/abs/2204.01697 (keeping, seems to be fine)
 - [x] allow for unet to be able to condition non-cross attention style as well
 - [x] speed up inference, read up on papers (ddim)
-- [ ] add inpainting ability using resampler from repaint paper https://arxiv.org/abs/2201.09865
-- [ ] become an expert with unets, cleanup unet code, make it fully configurable, port all learnings over to https://github.com/lucidrains/x-unet (test out unet² in ddpm repo) - consider https://github.com/lucidrains/uformer-pytorch attention-based unet
+- [x] add inpainting ability using resampler from repaint paper https://arxiv.org/abs/2201.09865
+- [ ] try out the nested unet from https://arxiv.org/abs/2005.09007 after hearing several positive testimonies from researchers, for segmentation anyhow
 - [ ] interface out the vqgan-vae so a pretrained one can be pulled off the shelf to validate latent diffusion + DALL-E2
 
 ## Citations